Upscaling Laboratory Formation Damage Test Data

Abstract

Abstract Laboratory formation damage testing is often used to assist in the selection of optimum drilling and completion fluids. Most test procedures (e.g. sand retention, return permeability) represent an attempt to simulate near wellbore conditions during well construction and production. Return permeability tests may enable comparison of fluids and drilling and completion methods, but by definition remain small scale simulations. The permeability changes measured may be indicative but are rarely used to predict the overall impact on well performance. What degree of permeability impairment is allowable? Some further interpretation is required, which cannot be provided for using classical nodal analysis or reservoir simulation methods. The impact of formation damage on overall well productivity or injectivity depends on the magnitude and distribution of the damage. In order to translate laboratory data into quantitative well predictions it is necessary to represent the degree of restriction and the geometry of the damaged zone or zones in a full well simulation. Computational Fluid Dynamics (CFD) simulations provide a means to upscale suitable laboratory test data to predict impact on well performance. Different magnitudes of damage derived from laboratory test data can be simulated and the impact on overall well performance predicted for different completion environments. Upscaling reveals that in some wells laboratory measured formation damage has little impact on well performance. Of course if damage is severe then impact is severe but it is critical to consider the well length, reservoir quality, drive mechanism and the well completion in order to fully understand the implications suggested by laboratory test data. This paper demonstrates through several case histories applications of CFD modelling to upscaling of laboratory measured formation damage. Moreover, the real implications for well and completion design are revealed. The value of laboratory testing is quantified and some interesting challenges to conventional wisdom are proposed.